Design and Implementation of Context Calculus in the GIPSY Environment
Bibliographic record
Abstract
GIPSY is a platform providing a framework for the compilation and execution of programs written in intensional programming languages of the Lucid family. While maintaining its use of intensionality, over the years, Lucid constantly underwent changes in its syntax, and its semantics is getting more and more generalized. Throughout this hectic evolution of the language, various systems for the evaluation of Lucid programs were developed. Due to lack of ability to adapt to the syntax and semantic changes of the language, all of them met with doom as new evolutions of the language were proposed. Set in this evolutionary aspect of Lucid, GIPSY aims at easing the development of the Lucid family of intensional programming languages by providing a common system into which variants of Lucid can be compiled and executed and, more interestingly, developed in the future. One of the latest evolutions of Lucid is the language Lucx, permitting the explicit use of contexts as first-class atomic entities. This paper presents the integration of Lucx's context calculus into GIPSY. We define the notion of context according to Lucx, its syntax and semantics, as well as operators on such contexts. We then present how context entities have been abstracted into implementation classes and embedded into GIPSY.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".